Data anonymization

The organizations produce and store all kinds of data in their information system every day. The diversity of data brings us to ask what are the best security policies for data, and how to validate a project. For example, data produced can be duplicated in training environments, test environments, critical data, confidential data, and sensitive data for the organization can be found on a protected hosting.

The question of data anonymization is indeed a crucial component in the data governance and cybersecurity approach in order to better anticipate the protection of your data.

Before implementing data anonymization, a classification of data is necessary, depending on the type of infrastructure used by an organization and on the increasing volume of data, an automatic classification may be able to speed up the process.
Before using scripts for data anonymization or dedicated anonymization services, it is necessary to :
  • List the tables and fields to anonymize
  • Label the fields with a type of attribute for preparation of the anonymization

The search for fields to anonymize is done by the classification of metadata and, or directly by a comprehension of data.